AI-powered Defect Detection and Prediction



Autonomous defect detection and prediction

Validata Sense.ai is a cognitive solution that is able to provide real-time classification on the root cause of the defects based on existing defect patterns and trends. In doing so, it results in fast routing of the defect to the right person or team for fixing. This helps to identify any regressions as soon as possible, accelerate development and drive higher quality and productivity for your projects.

Validata Sense.ai has fully automated the defect prediction and analysis process, leveraging machine learning algorithms and AI techniques to predict defects in real-time and determine the ‘next best action’. It can truly causate and automatically drive to the precise root cause of the issues, enabling DevOps to achieve lower development costs and a reduced project impact.

It is able to connect to your production and defect data, looks for common failure patterns and enables AI-generated recommendations and predictions, enhancing the ability of QA, DevOps, Project Managers and other IT specialists to manage their projects better and faster by getting contextual insights of the defect metrics.
The self-optimising AI engine continuously learns from the defect reports including text pre-processing, features extraction and selection and classifier building, and through natural language processing (NLP) and optical character recognition (OCR) it transforms the text giving more relevance and context.
Through proven Explainable AI it provides insight, confidence and transparency
on the AI automated decisions and recommendations.

By forecasting each week’s bugs, it brings efficiencies in planning and resourcing,
corrects the release policy and helps your development teams fix issues faster!
How it works


Collect
Collect data across your data sources - applications, databases and streams, storage, CRM, monitoring and analytics tools, as well as IT infrastructure.
Analyze
The system then analyses the collected data and continuously learns from their normal behavior and identifies deviations.
Correlate
It then groups correlated issues and identifies all events. This is a a great benefit as it leads to faster root cause analysis and reduce the time to resolution.
Act
It generates alerts and notifications so that each member of your team so your team can respond and act quickly
Feedback
Through feedback, the ML algorithms are continuously getting trained to further improve.
Key Capabilities


Automatically enriched bugs containing all relevant logs events and test data with metrics you need.
Predict where issues are most likely to occur and correlate data to quickly identify and resolve them
Automated Root Cause Analysis
Continuous feedback to development teams to improve ‘time to fix’
Text processing and re-assembling the defects description with semantic enrichment on the actual wording for accuracy and completeness
Creates taxonomies more efficiently
Optimizes release planning and corrects the release policy


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